import os
import sys
import logging
import paddle
import argparse
import functools
import math
import time
import numpy as np
import paddle.fluid as fluid
sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir)
from paddleslim.prune import AutoPruner
from paddleslim.common import get_logger
from paddleslim.analysis import flops
import models
from utility import add_arguments, print_arguments

_logger = get_logger(__name__, level=logging.INFO)

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size',       int,  64 * 4,                 "Minibatch size.")
add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
add_arg('model',            str,  "MobileNet",                "The target model.")
add_arg('pretrained_model', str,  "../pretrained_model/MobileNetV1_pretrained",                "Whether to use pretrained model.")
add_arg('lr',               float,  0.1,               "The learning rate used to fine-tune pruned model.")
add_arg('lr_strategy',      str,  "piecewise_decay",   "The learning rate decay strategy.")
add_arg('l2_decay',         float,  3e-5,               "The l2_decay parameter.")
add_arg('momentum_rate',    float,  0.9,               "The value of momentum_rate.")
add_arg('num_epochs',       int,  120,               "The number of total epochs.")
add_arg('total_images',     int,  1281167,               "The number of total training images.")
parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
add_arg('config_file',      str, None,                 "The config file for compression with yaml format.")
add_arg('data',             str, "imagenet",                 "Which data to use. 'mnist' or 'imagenet'")
add_arg('log_period',       int, 10,                 "Log period in batches.")
add_arg('test_period',      int, 10,                 "Test period in epoches.")
# yapf: enable

model_list = [m for m in dir(models) if "__" not in m]


def piecewise_decay(args):
    step = int(math.ceil(float(args.total_images) / args.batch_size))
    bd = [step * e for e in args.step_epochs]
    lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
    learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        momentum=args.momentum_rate,
        regularization=fluid.regularizer.L2Decay(args.l2_decay))
    return optimizer


def cosine_decay(args):
    step = int(math.ceil(float(args.total_images) / args.batch_size))
    learning_rate = fluid.layers.cosine_decay(
        learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs)
    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        momentum=args.momentum_rate,
        regularization=fluid.regularizer.L2Decay(args.l2_decay))
    return optimizer


def create_optimizer(args):
    if args.lr_strategy == "piecewise_decay":
        return piecewise_decay(args)
    elif args.lr_strategy == "cosine_decay":
        return cosine_decay(args)


def compress(args):

    train_reader = None
    test_reader = None
    if args.data == "mnist":
        import paddle.dataset.mnist as reader
        train_reader = reader.train()
        val_reader = reader.test()
        class_dim = 10
        image_shape = "1,28,28"
    elif args.data == "imagenet":
        import imagenet_reader as reader
        train_reader = reader.train()
        val_reader = reader.val()
        class_dim = 1000
        image_shape = "3,224,224"
    else:
        raise ValueError("{} is not supported.".format(args.data))

    image_shape = [int(m) for m in image_shape.split(",")]
    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                     model_list)
    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    # model definition
    model = models.__dict__[args.model]()
    out = model.net(input=image, class_dim=class_dim)
    cost = fluid.layers.cross_entropy(input=out, label=label)
    avg_cost = fluid.layers.mean(x=cost)
    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
    acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
    val_program = fluid.default_main_program().clone(for_test=True)
    opt = create_optimizer(args)
    opt.minimize(avg_cost)
    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    if args.pretrained_model:

        def if_exist(var):
            return os.path.exists(os.path.join(args.pretrained_model, var.name))

        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)

    val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
    train_reader = paddle.fluid.io.batch(
        train_reader, batch_size=args.batch_size, drop_last=True)

    train_feeder = feeder = fluid.DataFeeder([image, label], place)
    val_feeder = feeder = fluid.DataFeeder(
        [image, label], place, program=val_program)

    def test(epoch, program):
        batch_id = 0
        acc_top1_ns = []
        acc_top5_ns = []
        for data in val_reader():
            start_time = time.time()
            acc_top1_n, acc_top5_n = exe.run(
                program,
                feed=train_feeder.feed(data),
                fetch_list=[acc_top1.name, acc_top5.name])
            end_time = time.time()
            if batch_id % args.log_period == 0:
                _logger.info(
                    "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".
                    format(epoch, batch_id,
                           np.mean(acc_top1_n),
                           np.mean(acc_top5_n), end_time - start_time))
            acc_top1_ns.append(np.mean(acc_top1_n))
            acc_top5_ns.append(np.mean(acc_top5_n))
            batch_id += 1

        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
            epoch,
            np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
        return np.mean(np.array(acc_top1_ns))

    def train(epoch, program):

        build_strategy = fluid.BuildStrategy()
        exec_strategy = fluid.ExecutionStrategy()
        train_program = fluid.compiler.CompiledProgram(
            program).with_data_parallel(
                loss_name=avg_cost.name,
                build_strategy=build_strategy,
                exec_strategy=exec_strategy)

        batch_id = 0
        for data in train_reader():
            start_time = time.time()
            loss_n, acc_top1_n, acc_top5_n = exe.run(
                train_program,
                feed=train_feeder.feed(data),
                fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
            end_time = time.time()
            loss_n = np.mean(loss_n)
            acc_top1_n = np.mean(acc_top1_n)
            acc_top5_n = np.mean(acc_top5_n)
            if batch_id % args.log_period == 0:
                _logger.info(
                    "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {}; time: {}".
                    format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n,
                           end_time - start_time))
            batch_id += 1

    params = []
    for param in fluid.default_main_program().global_block().all_parameters():
        if "_sep_weights" in param.name:
            params.append(param.name)

    pruner = AutoPruner(
        val_program,
        fluid.global_scope(),
        place,
        params=params,
        init_ratios=[0.33] * len(params),
        pruned_flops=0.5,
        pruned_latency=None,
        server_addr=("", 0),
        init_temperature=100,
        reduce_rate=0.85,
        max_try_times=300,
        max_client_num=10,
        search_steps=100,
        max_ratios=0.9,
        min_ratios=0.,
        is_server=True,
        key="auto_pruner")

    while True:
        pruned_program, pruned_val_program = pruner.prune(
            fluid.default_main_program(), val_program)
        for i in range(1):
            train(i, pruned_program)
        score = test(0, pruned_val_program)
        pruner.reward(score)


def main():
    args = parser.parse_args()
    print_arguments(args)
    compress(args)


if __name__ == '__main__':
    main()
